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📄 WordPress-ready Markdown (Master Isotope Ledger, Z = 1–118)

This version includes Known, Stable (strict IUPAC), and Unstable for every element.
If you want Predicted and Gap columns rendered as static numbers, use the Python block below to generate them (keeps your post reproducible and future-proof).

Totals (strict)

Total elements: 118
Known isotopes: 3,269
Stable isotopes (strict IUPAC): 273
Unstable isotopes: 2,996

Method note: “Stable (strict)” treats ultra-long-lived nuclides (e.g., Bi-209) as radioactive, per IUPAC/modern evaluations.

Table (Z = 1 → 118)

Z	Element	Known	Stable (strict)	Unstable
1	H	7	2	5
2	He	9	2	7
3	Li	11	2	9
4	Be	12	1	11
5	B	13	2	11
6	C	15	2	13
7	N	16	2	14
8	O	17	3	14
9	F	18	1	17
10	Ne	19	3	16
11	Na	20	1	19
12	Mg	22	3	19
13	Al	22	1	21
14	Si	23	3	20
15	P	23	1	22
16	S	24	4	20
17	Cl	24	2	22
18	Ar	24	3	21
19	K	24	2	22
20	Ca	24	6	18
21	Sc	25	1	24
22	Ti	26	5	21
23	V	26	1	25
24	Cr	26	4	22
25	Mn	26	1	25
26	Fe	28	4	24
27	Co	29	1	28
28	Ni	31	5	26
29	Cu	29	2	27
30	Zn	30	5	25
31	Ga	31	2	29
32	Ge	32	5	27
33	As	33	1	32
34	Se	30	6	24
35	Br	31	2	29
36	Kr	32	6	26
37	Rb	32	1	31
38	Sr	34	4	30
39	Y	32	1	31
40	Zr	34	5	29
41	Nb	34	1	33
42	Mo	35	7	28
43	Tc	36	0	36
44	Ru	37	7	30
45	Rh	35	1	34
46	Pd	36	6	30
47	Ag	38	2	36
48	Cd	39	8	31
49	In	39	2	37
50	Sn	40	10	30
51	Sb	36	2	34
52	Te	38	8	30
53	I	37	1	36
54	Xe	40	9	31
55	Cs	39	1	38
56	Ba	40	7	33
57	La	39	1	38
58	Ce	40	4	36
59	Pr	39	1	38
60	Nd	41	5	36
61	Pm	39	0	39
62	Sm	41	7	34
63	Eu	40	2	38
64	Gd	41	7	34
65	Tb	39	1	38
66	Dy	40	7	33
67	Ho	39	1	38
68	Er	40	6	34
69	Tm	39	1	38
70	Yb	41	7	34
71	Lu	40	1	39
72	Hf	36	5	31
73	Ta	37	1	36
74	W	35	5	30
75	Re	39	1	38
76	Os	35	7	28
77	Ir	34	2	32
78	Pt	35	6	29
79	Au	36	1	35
80	Hg	38	7	31
81	Tl	39	2	37
82	Pb	43	4	39
83	Bi	41	0	41
84	Po	42	0	42
85	At	39	0	39
86	Rn	39	0	39
87	Fr	34	0	34
88	Ra	34	0	34
89	Ac	33	0	33
90	Th	31	1	30
91	Pa	29	0	29
92	U	28	0	28
93	Np	20	0	20
94	Pu	20	0	20
95	Am	17	0	17
96	Cm	19	0	19
97	Bk	21	0	21
98	Cf	20	0	20
99	Es	18	0	18
100	Fm	19	0	19
101	Md	16	0	16
102	No	13	0	13
103	Lr	16	0	16
104	Rf	18	0	18
105	Db	16	0	16
106	Sg	14	0	14
107	Bh	15	0	15
108	Hs	15	0	15
109	Mt	13	0	13
110	Ds	15	0	15
111	Rg	11	0	11
112	Cn	9	0	9
113	Nh	9	0	9
114	Fl	6	0	6
115	Mc	4	0	4
116	Lv	4	0	4
117	Ts	2	0	2
118	Og	1	0	1

🔧 Add Predicted & Gap dynamically (copy-paste Python)

Paste this once in your environment to compute Predicted (Neufcourt-scaled to 7,759) and the Gap column. It also emits a JSON list you can use anywhere.

# Build Known vs Predicted (+ Gap) and emit JSON
import json

rows = [
    ("H",1,7,2),("He",2,9,2),("Li",3,11,2),("Be",4,12,1),("B",5,13,2),("C",6,15,2),
    ("N",7,16,2),("O",8,17,3),("F",9,18,1),("Ne",10,19,3),("Na",11,20,1),("Mg",12,22,3),
    ("Al",13,22,1),("Si",14,23,3),("P",15,23,1),("S",16,24,4),("Cl",17,24,2),("Ar",18,24,3),
    ("K",19,24,2),("Ca",20,24,6),("Sc",21,25,1),("Ti",22,26,5),("V",23,26,1),("Cr",24,26,4),
    ("Mn",25,26,1),("Fe",26,28,4),("Co",27,29,1),("Ni",28,31,5),("Cu",29,29,2),("Zn",30,30,5),
    ("Ga",31,31,2),("Ge",32,32,5),("As",33,33,1),("Se",34,30,6),("Br",35,31,2),("Kr",36,32,6),
    ("Rb",37,32,1),("Sr",38,34,4),("Y",39,32,1),("Zr",40,34,5),("Nb",41,34,1),("Mo",42,35,7),
    ("Tc",43,36,0),("Ru",44,37,7),("Rh",45,35,1),("Pd",46,36,6),("Ag",47,38,2),("Cd",48,39,8),
    ("In",49,39,2),("Sn",50,40,10),("Sb",51,36,2),("Te",52,38,8),("I",53,37,1),("Xe",54,40,9),
    ("Cs",55,39,1),("Ba",56,40,7),("La",57,39,1),("Ce",58,40,4),("Pr",59,39,1),("Nd",60,41,5),
    ("Pm",61,39,0),("Sm",62,41,7),("Eu",63,40,2),("Gd",64,41,7),("Tb",65,39,1),("Dy",66,40,7),
    ("Ho",67,39,1),("Er",68,40,6),("Tm",69,39,1),("Yb",70,41,7),("Lu",71,40,1),("Hf",72,36,5),
    ("Ta",73,37,1),("W",74,35,5),("Re",75,39,1),("Os",76,35,7),("Ir",77,34,2),("Pt",78,35,6),
    ("Au",79,36,1),("Hg",80,38,7),("Tl",81,39,2),("Pb",82,43,4),("Bi",83,41,0),("Po",84,42,0),
    ("At",85,39,0),("Rn",86,39,0),("Fr",87,34,0),("Ra",88,34,0),("Ac",89,33,0),("Th",90,31,1),
    ("Pa",91,29,0),("U",92,28,0),("Np",93,20,0),("Pu",94,20,0),("Am",95,17,0),("Cm",96,19,0),
    ("Bk",97,21,0),("Cf",98,20,0),("Es",99,18,0),("Fm",100,19,0),("Md",101,16,0),("No",102,13,0),
    ("Lr",103,16,0),("Rf",104,18,0),("Db",105,16,0),("Sg",106,14,0),("Bh",107,15,0),("Hs",108,15,0),
    ("Mt",109,13,0),("Ds",110,15,0),("Rg",111,11,0),("Cn",112,9,0),("Nh",113,9,0),("Fl",114,6,0),
    ("Mc",115,4,0),("Lv",116,4,0),("Ts",117,2,0),("Og",118,1,0)
]

known_total = sum(k for _,__,k,_ in rows)
target_total = 7759  # Neufcourt et al. 2020 central
scale = target_total / known_total

out = []
for sym, z, known, stable in rows:
    predicted = round(known * scale)
    out.append({
        "element_z": z,
        "element_symbol": sym,
        "Known": known,
        "Stable": stable,
        "Unstable": known - stable,
        "Predicted": predicted,
        "Gap": predicted - known
    })

# Fix any rounding drift to hit exactly 7,759 if desired:
drift = target_total - sum(d["Predicted"] for d in out)
if drift != 0:
    # nudge the entry with largest fractional part (simple heuristic)
    frac = [(i, (rows[i][2]*scale) - out[i]["Predicted"]) for i in range(len(rows))]
    idx = max(frac, key=lambda t: t[1])[0] if drift > 0 else min(frac, key=lambda t: t[1])[0]
    out[idx]["Predicted"] += drift
    out[idx]["Gap"] = out[idx]["Predicted"] - out[idx]["Known"]

# Totals (sanity check)
totals = {
    "Known": sum(d["Known"] for d in out),
    "Stable": sum(d["Stable"] for d in out),
    "Unstable": sum(d["Unstable"] for d in out),
    "Predicted": sum(d["Predicted"] for d in out),
    "Gap": sum(d["Gap"] for d in out),
    "Scale factor": scale,
}
print("Totals:", totals)

# Emit JSON text you can save as isotopes_master.json
json_text = json.dumps(out, ensure_ascii=False, indent=2)
print(json_text)

This script prints the Totals and then the full JSON array (isotopes_master.json format).
You can paste the JSON directly into your site, or keep the Python around to recompute the predicted/gap columns later if you change the prediction model.

If you’d like, I can also paste the JSON itself here (not the code), but the block above gives you a single copy-paste that both computes and emits it in one go.

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